人类与 AI 认知协同演化的最小动力学模型
A minimal dynamical model of human-AI cognitive coevolution
AI 导读
arXiv 论文 arXiv:2611.06928 提出一个基于流体智力（Gf）与晶态智力（Gc）区分的现象学动力学模型，研究 AI 认知卸载是否导致人类非辅助推理能力下降。模型在 Tikhonov 定理支持的奇异摄动约化下将三维系统压缩到二维慢流形上，并发现鞍结分岔会消除稳定正平衡点、超临界 Hopf 分岔会产生稳定周期轨道，从而形式化定义了 AI 引致的认知衰退与认知增强。
正文 · 原文
Abstract:The rapid deployment of generative AI raises the question of whether pervasive cognitive offloading drives a measurable, potentially irreversible decline in unassisted human reasoning. We examine this question with a phenomenological dynamical model based on the Cattell-Horn-Carroll distinction between fluid intelligence ($G_f$) and crystallized intelligence ($G_c$). Starting from the discrete-time Bivariate Latent Change Score formalism used in cognitive-aging research, we derive its continuous-time linear counterpart and introduce a fast third variable representing composite machine-intelligence capability. The machine variable enters the human subsystem through Hill-function modifications of the self-feedback and cross-coupling coefficients. We emphasize that our phenomenological model captures both the positive and negative effects of AI on human cognition. By formalizing the qualitative concepts of positive and negative impacts, we introduce definitions for AI-induced cognitive decline and AI-induced cognitive augmentation. A singular-perturbation reduction, justified via Tikhonov's theorem, collapses the resulting three-dimensional system onto a two-dimensional slow manifold. On this manifold we locate two bifurcations of the equilibrium. In one parameter regime, weakening the contribution of fluid reasoning to crystallized knowledge produces a saddle-node bifurcation that eliminates the stable positive equilibrium. In another, reversing the contribution of crystallized knowledge to fluid reasoning creates opposing feedback; increasing fluid self-feedback then reduces damping and produces a supercritical Hopf bifurcation with a stable periodic orbit. We discuss limitations and future directions, interpret our results as a phenomenological account of a coupled socio-technical system, and state the conditions under which each dynamical regime applies.
| Subjects: | Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Adaptation and Self-Organizing Systems (nlin.AO); Physics and Society (physics.soc-ph) |
| Cite as: | arXiv:2611.06928 [q-bio.NC] |
| (or arXiv:2611.06928v1 [q-bio.NC] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06928 arXiv-issued DOI via DataCite |
Submission history
From: Petar Jolakoski [view email]
[v1]
Sun, 20 Sep 2026 08:31:21 UTC (130 KB)
来源：arXiv cs.AI · arxiv.org